{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simpson's paradox\n",
    "\n",
    "There's probably already code that does this, but wtf, this will be fun."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def oval(xbase, ybase):\n",
    "    x = np.random.normal(scale = 2.8, size = 40)\n",
    "    y = np.random.normal(scale = 1.2, size = 40)\n",
    "    x1 = (x * 0.66) - (y * 0.34)\n",
    "    y1 = -(x * 0.34) + (y * 0.66)\n",
    "    return x1 + xbase, y1 + ybase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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eAh069F7Yenu71TaAnrM9QEhScXGxiouLw98IICSXy9Xr7vrOYeThh4t14kTg0IhvQmS+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\n2P8BTZd570oBKxMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118041f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x, y = oval(0, 0)\n",
    "plt.scatter(x, y)\n",
    "plt.xlim(-10, 10)\n",
    "plt.ylim(-10, 10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wZXoA8ac//cnsJRARhSYxl8Gz6VRUlPM4wsgb9k+HAf94W9trHQ5nUufT97vnTLA6I6xY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\nEBERkW4MIIiIiEi3/w/s//a5QMRaywAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118041278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x1, y1 = oval(-5, -5)\n",
    "x2, y2 = oval(-2.5, -2.5)\n",
    "x3, y3 = oval(0, 0)\n",
    "x4, y4 = oval(2.5, 2.5)\n",
    "x5, y5 = oval(5, 5)\n",
    "\n",
    "plt.figure(figsize = (6, 4))\n",
    "sns.set_style(\"white\")\n",
    "plt.scatter(x1, y1, color = 'darkmagenta')\n",
    "plt.scatter(x2, y2, color = 'slateblue')\n",
    "plt.scatter(x3, y3, color = 'springgreen')\n",
    "plt.scatter(x4, y4, color = 'gold')\n",
    "plt.scatter(x5, y5, color = 'tomato')\n",
    "sns.despine()\n",
    "plt.xlim(-10, 10)\n",
    "plt.ylim(-10, 10)\n",
    "plt.plot([-10, 10], [-10, 10], 'k--')\n",
    "plt.savefig('../manuallists/figures/stanford/simpsonparadox.png', bbox_inches = 'tight', dpi = 400)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
